The multimedia broadcaster is commonly faced with two problems when marketing its programs. The first problem is effectively measuring customer demand for multimedia broadcasts. The second problem is effectively utilizing bandwidth to deliver multimedia broadcasts.
There is no simple solution to the first problem because measuring customer demand for multimedia broadcasts, such as television programs, is difficult to do with any degree of precision. For example, past successful and unsuccessful programs may be used by the broadcaster as a guideline for what will be successful, but recycling past programs may lead as equally to failure as to success.
Effectively measuring customer demand for pay per view broadcasts (which term as used herein includes Video on Demand broadcasts) is at least as difficult as measuring demand for free content. Additionally, the broadcaster may incur a certain amount of risk with pay per view programming. This risk may be incurred because pay per view content needs to be attractive enough for customers to be willing to pay for the content. Thus, the broadcaster may need to invest larger sums to develop pay per view content than it would invest to develop free content, and so incur greater risk in developing pay per view content than in developing free content.
In order to attempt to effectively measure customer demand for pay per view, and to minimize the risk in developing pay per view programming, the pay per view broadcaster may attempt to determine what programming will be attractive to customers in a number of ways. For example, as with free broadcasts, the pay per view broadcaster may attempt to develop and present works with content that has previously been shown to be desired by customers. The pay per view broadcaster might also attempt to analyze historical consumer subscription data and build a statistical model to predict customer demand for future programming.
Neither the content based model nor statistical data model method, however, is especially effective in measuring customer demand and minimizing risk. Neither model is effectively able to establish, before providing a program, customer demand for the program with any degree of precision. For example, the statistical model may fail to include customer consumption of free content in its model, thus excluding data that would assist in constructing the model.
Broadcasters may attempt to combine both models to improve the accuracy of any method used, however, even with a combination of both models, limitations remain. For example, as long as any such models depend upon historical data they may “miss the mark” because the models use that historical data to predict future performance. For example, since the historical data was collected, customer tastes may have changed, the market may have been saturated with a particular type of product, etc., and so the historical data would not serve as an accurate predictor of future performance.
Thus the pay per view broadcaster is often faced with a dilemma. The broadcaster needs to develop content and devote its broadcast resources to the dissemination of the content, yet the broadcaster also needs to be assured of some rate of return on the programs it is developing and broadcasting. Presently, the art lacks a simple, effective, resolution of that dilemma.
Complicating matters even further is the evolving nature of broadcast technology. For example, presently in-home television technology is primarily comprised of analog systems, with passive analog receptors (e.g., television receivers) and analog storage capabilities (e.g., video cassette recorders.) However, digital technology will likely begin to assume increasing importance in the area of in-home television technology. Digital technology, with its greater ability to deliver content, may increase the broadcaster's dilemma. The broadcaster needs to deliver more content because it has more channels to fill and so it needs to develop more content and devote more resources to the delivery of the content. Yet, that greater investment in content for multiple channels might lead to a greater amount of risk than content for a single channel, because the broadcaster has devoted more resources to multiple show development. Indeed, if the broadcaster repeatedly generates multiple shows for those multiple channels without accurately gauging consumer demand, it may well cease operating after a period of time.
The problem of measuring customer demand and minimizing risk is, as was noted above, only one problem the multimedia broadcaster faces. The second problem is effectively utilizing bandwidth to deliver multimedia broadcasts.
Effectively utilizing bandwidth to deliver multimedia broadcasts is a problem because channels are limited, even in a digital technology delivery environment. Maximizing each channel means ensuring the content delivered through the channel is the most desirable and appropriate content for the customer.
Of course, this problem is related to the first problem, that of measuring customer demand. If customer demand is properly measured, it is more likely than not that the bandwidth is effectively utilized. Still, effective utilization and maximal utilization may be different. For example, content may be attractive enough to reach a baseline audience, yet that content may not be the most effective in attracting the maximal audience. Given the limited scope of channels or delivery systems, it would be in the broadcaster's best interest to use each channel to attract the maximal audience for the content being broadcast on that channel.
The art to date primarily uses the mechanisms described above, that is, those that attempted to measure customer demand, to attempt to maximize content. Yet, as noted above, the industry would benefit from a more precise measurement system in its attempt to maximize utilization of its limited delivery systems.